산업공학과

[Research Interests]
Process Mining, Data Mining, Anomaly Detection, Blockchain

[Description]

This lab focuses on the application of machine learning and computational intelligence techniques (e.g., classification/regression, deep learning, genetic algorithms and other evolutionary techniques, statistical anomaly detection) to the analysis of business process event logs. These are logs generated by the information systems that support the execution of business processes in organizations. We solve problems like predicting the outcome of the execution of business processes, predicting the activities that will be executed next in a process, or identifying anomalies in event logs, considering also the event streaming perspective.

[Research Interests]
Business Analytics, Statistical Quality Control, Anomaly Detection, Data Mining and Machine Learning, Design of Experiments, Robust Parameter Design, Demand Forecasting, Predictive Analytics

[Description]
Dr. Kim's research interests are in the broad areas of data science and business analytics. A major focus of his research is in developing novel statistical methods for solving complex engineering problems. He has several years of consulting experience in solving real business problems in industries.

[Research Interests]

Knowledge Discovery on the Representation, Prediction, Generation, and Control by Machines Applied Data Science and the Intelligence Development for Real-world Service Engineering

[Description]

We focus on developing data analytics methods to achieve learning tasks (i.e., knowledge discovery from data), such as representation, generation, prediction, and clustering. Based on such methods, we are also interested in solving real-world service problems with firms and governments (i.e., service engineering with data), including item recommendation, behavioral intervention, process monitoring, and service improvement.

[Research Interests]

GPU-accelerated and AI-enhanced mathematical optimization, Integration of mathematical optimization with AI, Energy system optimization

[Description]

ACCOL (ACCelerated Optimization Laboratory) aims at developing accelerated mathematical optimization algorithms via GPUs and AI and improving the quality of AI solutions via mathematical optimization. To achieve this, we study i) GPU-accelerated distributed large-scale mathematical optimization algorithms; ii) the integration of mathematical optimization with AI; and iii) a computational framework that provides easy access to our technology. Our recent research results have been applied to large-scale power system optimization and biobank analysis.

[Research Interests]

Financial Engineering, Financial Optimization, Financial Data Analysis, Financial Planning

[Description]

We study quantitative approaches to financial planning of individuals and institutions. 

Most research topics can be categorized into three: 

(1) making optimal investment decisions using optimization and machine learning, 

(2) financial market modeling using machine learning techniques, and 

(3) investor data analysis using machine learning techniques. By developing advanced theories and practical technologies, we aim to make it possible for everyone to receive customized life-time financial planning services.

[Research Interests]

Sequential Decision Making, Bandit Algorithms, Causal Inference, Missing Data Analysis

[Description]

Our research interests are focused on statistical approaches to the sequential decision problem. The multi-armed bandit (MAB) problem formulates the sequential decision problem in which a learner is sequentially faced with a set of available actions, chooses an action, and receives a random reward in response. In our lab, we integrate online learning and optimization techniques to develop algorithms that efficiently learn the reward model while maximizing the rewards. We also apply the developed algorithms to real tasks such as recommendation systems and mobile health apps. We also use causal inference to evaluate the performance of multi-armed bandit algorithms in a retrospective way.

[Research Interests]

Stochastic and Nonconvex Optimization, Generative Models, Mathematical Finance, AI application in Finance and Insurance

[Description]

The research of Prof. Dong-Young Lim's lab is focused on stochastic optimization algorithms, nonconvex optimization, and their applications in finance and insurance. In particular, we are interested in quantitative risk management in financial markets, the development of efficient algorithms for large-scale nonconvex optimization, the study of theoretical properties of such algorithms.
Some of our current research projects are - Diffusion-based algorithms for nonconvex optimization and generative model, - MCMC algorithms, - AI application in finance and insurance.

[Research Interests]

Privacy-preserving machine learning, fairness-aware machine learning, security-enhanced machine learning

[Description]

Our research is focused on addressing the interconnected challenges of privacy, fairness, and security to promote the safe use of artificial intelligence (AI) algorithms in real-world systems. Our goal is to develop innovative solutions that enable privacy-preserving, fairness-aware, and security-enhanced machine learning. To achieve this goal, we are pursuing two key problem thrusts: (i) We are developing comprehensive approaches to ensure the reliability of AI while considering security and privacy threats. (ii) We are addressing the need for realistic threat models and evaluation for security-aware algorithms. We are committed to advancing the field of artificial intelligence in a responsible and ethical manner. We believe that these three pillars are critical for building AI systems that are safe and beneficial for individuals, industry, and society.

[Research Interests]

Reinforcement Learning, Multi-Agent Systems, AI-based Decision-making, Autonomous Systems and Operations, Representation Learning, Robot Learning and Physical AI

[Description]

In this era of AI transformation, a wide range of research is actively integrating AI into domains such as manufacturing, finance, defense, and aerospace, with a strong focus on autonomous decision-making. Within this trend, ASDL aims to develop autonomous systems and enhance decision-making capabilities across diverse domains. Our lab conducts research in both AI foundations (e.g., multi-agent reinforcement learning, representation learning, robot learning, and lifelong learning) and AI applications (e.g., defense systems, smart manufacturing, and complex network systems).